IPHO-Journal of Advance Research in Mathematics And Statistics
https://iphopen.org/index.php/ms
<p><strong>IPHO-Journal of Advance Research in Mathematics And Statistics, <a href="https://portal.issn.org/resource/ISSN/3050-9068">(e-ISSN 3050-9068, p-ISSN 3050-9335)</a></strong> Without mathematics, there’s nothing you can do. Everything around you is mathematics. Everything around you is numbers. Mathematics has been regarded as the backbone of scientific and technological development without which no nation can attain any sustainable development. Mathematics has been referred as the language of science, as everything man does involve mathematics, from the formulas we use to model the world, to the trials and measurements we use to test and apply our models. etc</p>IPHO Journalen-USIPHO-Journal of Advance Research in Mathematics And Statistics3050-9335<p>Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties and that the Article has not been published elsewhere. Author(s) agree to the terms that the <strong>IPHO Journal</strong> will have the full right to remove the published article on any misconduct found in the published article.</p>On the Odd Burr III-Type II Generalized Exponential-G Family of Distributions: Properties with Applications to Failure Data
https://iphopen.org/index.php/ms/article/view/302
<p>This study introduces the odd Burr III-Type II Generalized ExponentialG (OBIII-TIIGE-G) family of distributions, a flexible statistical framework designed to model diverse data geometries. By synthesizing the<br>structural strengths of the odd Burr III-G and the type II general<br>exponential-G families, the proposed model addresses critical gaps in<br>existing distributions, particularly their inability to simultaneously capture monotonic and non-monotonic hazard rates. The OBIII-TIIGEG family offers analytical tractability, with closed-form expressions for<br>quantile functions, moments, and hazard rate dynamics, enabling robust reliability assessments. Monte Carlo simulations validate the consistency and efficiency of maximum likelihood estimators, showing reduced bias and root mean square error as sample sizes increase. Applied<br>to real-world failure datasets—carbon fiber breaking stress and silicon<br>nitride fracture toughness, the model demonstrates superior goodnessof-fit over six competing distributions, evidenced by lower goodnessof-fit statistics with higher p-values. Its ability to adapt to symmetric,skewed, and heavy-tailed data, coupled with identifiable parameters and<br>precise estimation, positions it as a vital tool for reliability engineering<br>and materials science. This research advances distribution theory and<br>provides practitioners with a versatile solution for modeling complex<br>failure-time phenomena.</p>Wilbert NkomoJoseph ManyembaLiberty MudzengerereDominic MhiniIsaac PasipanodyaTakesure Nyakuamba
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2025-06-262025-06-26306012010.5281/zenodo.15743517